# 1.4: Algorithms

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4

Invisible, Irreversible, and Infinite

## How can computers carry bias?

Many people think computers and algorithms are neutral – racism and sexism are not programmers’ problems. In the case of Tay’s programmers, this false belief enabled more hate speech online and led to the embarrassment of their employer. Human-crafted computer programs mediate nearly everything humans do today, and human responses are involved in many of those tasks. Considering the near-infinite extent to which algorithms and their activities are replicated, the presence of human biases is a devastating threat to computer-dependent societies in general and to those targeted or harmed by those biases in particular.

Problems like these are rampant in the tech industry because there is a damaging belief in US (and some other) societies that the development of computer technologies is antisocial, and that some kinds of people are better at it than others. As a result of this bias in tech industries and computing, there are not enough kinds of people working on tech development teams: not enough women, not enough people who are not white, not enough people who remember to think of children, not enough people who think socially.

Remember Google Glass? You may not; that product failed because few people wanted interaction with a computer to come between themselves and eye contact with humans and the world. People who fit the definition of “tech nerd” fell within this small demographic, but the sentiment was not shared by the broader community of technology users. Critics labeled the unfortunate people who did purchase the product as “glassholes.”

## Case study: microcelebrity in the age of algorithms

Student Content

Viral. Meaning that millions of people saw this content and reposted or shared it with their friends, their families, and even the media. At some point in our lives, I am sure we have all pondered about how life would be if we were famous. Newfound fame- countless fans and followers, brand deals, being recognized in public. It all sounds great, right? Well, maybe not.

With the new surge of upcoming apps such as TikTok, along with Instagram, Snapchat, Twitter, and many other platforms, come more opportunities for different content and creators to spread. Our society is now so deeply rooted in the media with the ultimate hope to reap the benefit of being seen. Social media was primarily created to provide an outlet for friends and families to keep connected. While this is still one of the many affordances of social media, our society has turned to using the platform for more selfish reasons- such as the fame granted when going viral.

There are some noticeable pros and cons that are intertwined with media spreadability. This term highlights how media is continually spread and then passed on to others, continuing the chain.

At this point, almost everyone has had exposure through the media. In terms of spreadability, exposure happens much more quickly. One second a video is posted and the next, it could have thousands of views. This was the case with now-famous influencer, Emma Chamberlain.

Currently, Emma Chamberlain has accumulated an astounding total of nearly ten million subscribers and counting. At only 19, she has established a huge platform for herself and when she started as a young high school girl nearly three years ago, I can guarantee she had no idea what was in store for her in terms of success. As of now, she has won three awards for her Youtube career: a People’s Choice Award, a Shorty Award, and lastly, a Teen Choice Award. Her breakout Youtube fame allowed her to then write her own book, create a podcast, and even make and sell merchandise. All of this success due to a few viral videos that skyrocketed a young girl’s career. How did her videos spread so quickly? Was her content really that appealing to her audience? Did she face any backlash? What type of content tends to go viral? Chelsea Galvin is here to give her insight on these types of questions we all have.

Emma Chamberlain was just an ordinary girl from Belmont, California. Who else is a teenage girl from Belmont, California? My roommate C. She was a primary witness in Emma Chamberlain’s claim to fame. Both girls are from the same hometown, attended the same high school, and had the same classes.

C is no stranger to the realm of social media. She uses popular apps such as Instagram, TikTok, and Snapchat (her favorite as of now). She is familiar with the various different Algorthims that appear on her explorer and “for you” pages. She typically watches videos about house decor, food, and videos of friends just having fun.

She believes that Emma Chamberlain’s content was relevant for teen girls today. Her content is “different from mainstream media and what we usually see on youtube” and is associated with certain algorithms that relate to young teens today. Ultimately, Emma Chamberlain became so well known for her unstaged and realistic content that she is now easily recognizable by so many people.

Although having a presence in the media may seem extremely desirable, there are always obstacles and hardships that must be overcome. Cancel culture. Currently, this is a big part of having a media presence. Individuals must always be aware of what they post in order to avoid upsetting others, whether it is intentional or not.

As C describes it, “a lot of attention brings a lot of people just wanting to hate or ruin things for people” and I totally agree. We are all human and we all make mistakes but when those mistakes resurface online due to spreadability and a face in the media, those select individuals have a harder time than those who are not in the spotlight.

Ultimately anyone’s content can spread and go viral, it is just a matter of time and good, relatable videos – like Emma Chamberlain posts. Both C and I believe that it is important to be educated on this topic, especially in times like this where social media plays a large role in our daily lives. We have both seen the more obstructive side of social media when content goes viral and we agree that it’s vital to be prepared for the outcome.

It was a joy to chat with my roommate and learn more about our perspectives on the media and what content is specific to our two feeds. I learned so much from her and her story and it really helped me conceptualize the term spreadability and how it occurs in reality.

Lily was born and raised in Southern California, or more specifically, Pasadena, California. Her whole life she has been in the same area and absolutely loves the opportunities given to her while living in LA County. She has grown up with two brothers and two amazing dogs. Her favorite hobbies include exploring new cities, taking photos, trying new restaurants, going to the beach, and spending quality time with her loved ones. Currently, Lily is living in Tucson to further her education at the University of Arizona where she is studying Communication. She hopes to pursue event planning or advertising.

Respond to this case study… how might a creator change their content to affect a platform’s algorithm? How can creators and users learn more about the algorithms affecting them? How might platforms benefit from sharing more information about their algorithms? Why might they want to keep some things hidden from users?

# Exacerbating Bias in Algorithms: The Three I’s

In its early years, the internet was viewed as a utopia, an ideal world that would permit a completely free flow of all available information to everyone, equally. John Perry Barlow’s 1996 Declaration of the Independence of Cyberspace represents this utopian vision, in which the internet liberates users from all biases and even from their own bodies (at which human biases are so often directed). Barlow’s utopian vision does not match the internet of today. Our social norms and inequalities accompany us across all the media and sites we use, and worsened in a climate where information value is determined by marketability and profit, as Sociologist Zeynep Tufecki explains in this Ted Talk.

Because algorithms are built on human cooperation with computing programs, human selectivity and human flaws are embedded within algorithms. Humans as users carry our own biases, and today there is particular concern that algorithms pick up and spread these biases to many, many others. They even make us more biased by hiding results that the algorithm calculates we may not like. When we get our news and information from social media, invisible algorithms consider our own biases and those of friends in our social networks to determine which new posts and stories to show us in search results and news feeds. The result for each user can be called their echo chamber or as author Eli Pariser describes it, a filter bubble in which we only see news and information we like and agree with, leading to political polarization.

Although algorithms can generate very sophisticated recommendations, algorithms do not make sophisticated decisions. When humans make poor decisions, they can rely on themselves or on other humans to recognize and reverse the error; at the very least, a human decision-maker can be held responsible. Human decision-making often takes time and critical reflection to implement, such as the writing of an approved ordinance into law. When algorithms are used in place of human decision-making, I describe what ensues as The three I's: Algorithms’ decisions become invisible, irreversible, and infinite. Most social media platforms and many organizations using algorithms will not share how their algorithms work; for this lack of transparency, they are known as black box algorithms.

## Exposing Invisible Algorithms: Pro Publica

Journalists at Pro Publica are educating the public on what algorithms can do by explaining and testing black box algorithms. This work is particularly valuable because most algorithmic bias is hard to detect for small groups or individual human users. Studies like ProPublica’s presented in the “Breaking the Black Box” series (below) have been based on groups systematically testing algorithms from different machines, locations, and users. Using investigative journalism, Pro Publica has also found that algorithms used by law enforcement are significantly more likely to label African Americans as High Risk for reoffending and white Americans as Low Risk.

## Fighting Unjust Algorithms

Algorithms are laden with errors. Some of these errors can be traced to the biases of those of developed them, as when a facial recognition system meant for global implementation is only trained using data sets from a limited population (say, predominantly white or male). Algorithms can become problematic when they are hacked by groups of users, like Microsoft’s Tay was. Algorithms are also grounded in the values of those who shape them, and these values may reward some involved while disenfranchising others.

Despite their flaws, algorithms are increasingly used in heavily consequential ways. They predict how likely a person is to commit a crime or default on a bank loan based on a given data set. They can target users with messages on social media that are customized to fit their interests, their voting preferences, or their fears. They can identify who is in photos online or in recordings of offline spaces.

Confronting the landscape of increasing algorithmic control is activism to limit the control of algorithms over human lives. Below, read about the work of the Algorithmic Justice League and other activists promoting bans on facial recognition. And consider: What roles might algorithms play in your life that may deserve more attention, scrutiny, and even activism?

## The Algorithmic Justice League vs facial recognition tech in Boston

MIT Computer Scientist and “Poet of Code” Joy Buolamwini heads the Algorithmic Justice League, an organization making remarkable headway into fighting facial recognition technologies, whose work she explains in the first video below. On June 9th, 2020, Buolamwini and other computer scientists presented alongside citizens at Boston City Council meeting in support of a proposed ordinance banning facial recognition in public spaces in the city. Held and shared by live stream during COVID-19, footage of this meeting offers a remarkable look at the value of human advocacy in shaping the future of social technologies. The second video below should be cued to the beginning of Buolamwini’s testimony half an hour in. Boston’s City Council subsequently voted unanimously to ban facial recognition technologies by the city.

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# Core Concepts and Questions

## Core Concepts

#### algorithm

a step-by-step set of instructions for getting something done to serve humans, whether that something is making a decision, solving a problem, or getting from point A to point B (or point Z)

#### why computers seem so smart today

cooperation from human software developers, and cooperation on the part of users

#### biases

assumptions about a person, culture, or population

#### filter bubble

a term coined by Eli Pariser, also called an echo chamber. A phenomenon in which we only see news and information we like and agree with, leading to political polarization

black box algorithms

the term used when processes created for computer-based decision-making is not shared with or made clear to outsiders

The three I's

algorithms’ decisions can become invisible, irreversible, and infinite

## Core Questions

### A. Questions for qualitative thought

1. Write and/or draw an algorithm (or your best try at one) to perform an activity you wish you could automate. Doing the dishes? Taking an English test? It’s up to you.
2. Often there are spaces online that make one feel like an outsider, or like an insider. Study an online space that makes you feel like one of these – how it that outsider or insider status being communicated to you, or to others?
3. Consider the history of how you learned whatever you know about computing. This could mean how you came to understand key terms, searching online simple programs, coding, etc. Then, reinvent that history if you’d learned all you wish you knew about computing at the times and in the ways you feel you should have learned them.

### B. Review: Let’s test how well you’ve been programmed. (Mark the best answers.)

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# Hear It: Electronic Freedom Foundation’s “Algorithms for a Just Future”

EPISODE SUMMARY

The United States already has laws against redlining, but companies can still use other data to advertise goods and services to you—which can have big implications for the prices you see.

EPISODE NOTES

One of the supposed promises of AI was that it would be able to take the bias out of human decisions, and maybe even lead to more equity in society. But the reality is that the errors of the past are embedded in the data of today, keeping prejudice and discrimination in. Pair that with surveillance capitalism, and what you get are algorithms that impact the way consumers are treated, from how much they pay for things, to what kinds of ads they are shown, to if a bank will even lend them money. But it doesn’t have to be that way, because the same techniques that prey on people can lift them up. Vinhcent Le from the Greenlining Institute joins Cindy and Danny to talk about how AI can be used to make things easier for people who need a break. In this episode you’ll learn about:

• Redlining—the pernicious system that denies historically marginalized people access to loans and financial services—and how modern civil rights laws have attempted to ban this practice.
• How the vast amount of our data collected through modern technology, especially browsing the Web, is often used to target consumers for products, and in effect recreates the illegal practice of redlining.
• The weaknesses of the consent-based models for safeguarding consumer privacy, which often mean that people are unknowingly waving away their privacy whenever they agree to a website’s terms of service.
• How the United States currently has an insufficient patchwork of state laws that guard different types of data, and how a federal privacy law is needed to set a floor for basic privacy protections.
• How we might reimagine machine learning as a tool that actively helps us root out and combat bias in consumer-facing financial services and pricing, rather than exacerbating those problems.
• The importance of transparency in the algorithms that make decisions about our lives.
• How we might create technology to help consumers better understand the government services available to them.

http://dig.ccmixter.org/files/djlang59/37792

Drops of H2O ( The Filtered Water Treatment ) by J.Lang (c) copyright 2012 Licensed under a Creative Commons Attribution (3.0) license.http://dig.ccmixter.org/files/djlang59/37792 Ft: Airtone

http://dig.ccmixter.org/files/zep_hurme/59681

http://dig.ccmixter.org/files/airtone/59721

## Read it: Do social media algorithms erode our ability to make decisions freely? The jury is out

Social media algorithms, artificial intelligence, and our own genetics are among the factors influencing us beyond our awareness. This raises an ancient question: do we have control over our own lives? This article is part of The Conversation’s series on the science of free will.

Have you ever watched a video or movie because YouTube or Netflix recommended it to you? Or added a friend on Facebook from the list of “people you may know”?

And how does Twitter decide which tweets to show you at the top of your feed?

These platforms are driven by algorithms, which rank and recommend content for us based on our data.

As Woodrow Hartzog, a professor of law and computer science at Northeastern University, Boston, explains:

If you want to know when social media companies are trying to manipulate you into disclosing information or engaging more, the answer is always.

So if we are making decisions based on what’s shown to us by these algorithms, what does that mean for our ability to make decisions freely?

## What we see is tailored for us

An algorithm is a digital recipe: a list of rules for achieving an outcome, using a set of ingredients. Usually, for tech companies, that outcome is to make money by convincing us to buy something or keeping us scrolling in order to show us more advertisements.

The ingredients used are the data we provide through our actions online – knowingly or otherwise. Every time you like a post, watch a video, or buy something, you provide data that can be used to make predictions about your next move.

These algorithms can influence us, even if we’re not aware of it. As the New York Times’ Rabbit Hole podcast explores, YouTube’s recommendation algorithms can drive viewers to increasingly extreme content, potentially leading to online radicalisation.

Facebook’s News Feed algorithm ranks content to keep us engaged on the platform. It can produce a phenomenon called “emotional contagion”, in which seeing positive posts leads us to write positive posts ourselves, and seeing negative posts means we’re more likely to craft negative posts — though this study was controversial partially because the effect sizes were small.

Also, so-called “dark patterns” are designed to trick us into sharing more, or spending more on websites like Amazon. These are tricks of website design such as hiding the unsubscribe button, or showing how many people are buying the product you’re looking at right now. They subconsciously nudge you towards actions the site would like you to take.

## You are being profiled

Cambridge Analytica, the company involved in the largest known Facebook data leak to date, claimed to be able to profile your psychology based on your “likes”. These profiles could then be used to target you with political advertising.

“Cookies” are small pieces of data which track us across websites. They are records of actions you’ve taken online (such as links clicked and pages visited) that are stored in the browser. When they are combined with data from multiple sources including from large-scale hacks, this is known as “data enrichment”. It can link our personal data like email addresses to other information such as our education level.

These data are regularly used by tech companies like Amazon, Facebook, and others to build profiles of us and predict our future behaviour.

## You are being predicted

So, how much of your behaviour can be predicted by algorithms based on your data?

Our research, published in Nature Human Behaviour last year, explored this question by looking at how much information about you is contained in the posts your friends make on social media.

Using data from Twitter, we estimated how predictable peoples’ tweets were, using only the data from their friends. We found data from eight or nine friends was enough to be able to predict someone’s tweets just as well as if we had downloaded them directly (well over 50% accuracy, see graph below). Indeed, 95% of the potential predictive accuracy that a machine learning algorithm might achieve is obtainable just from friends’ data.

Our results mean that even if you #DeleteFacebook (which trended after the Cambridge Analytica scandal in 2018), you may still be able to be profiled, due to the social ties that remain. And that’s before we consider the things about Facebook that make it so difficult to delete anyway.

We also found it’s possible to build profiles of non-users — so-called “shadow profiles” — based on their contacts who are on the platform. Even if you have never used Facebook, if your friends do, there is the possibility a shadow profile could be built of you.

On social media platforms like Facebook and Twitter, privacy is no longer tied to the individual, but to the network as a whole.

## No more free will? Not quite

But all hope is not lost. If you do delete your account, the information contained in your social ties with friends grows stale over time. We found predictability gradually declines to a low level, so your privacy and anonymity will eventually return.

While it may seem like algorithms are eroding our ability to think for ourselves, it’s not necessarily the case. The evidence on the effectiveness of psychological profiling to influence voters is thin.

Most importantly, when it comes to the role of people versus algorithms in things like spreading (mis)information, people are just as important. On Facebook, the extent of your exposure to diverse points of view is more closely related to your social groupings than to the way News Feed presents you with content. And on Twitter, while “fake news” may spread faster than facts, it is primarily people who spread it, rather than bots.

Of course, content creators exploit social media platforms’ algorithms to promote content, on YouTube, Reddit and other platforms, not just the other way round.

At the end of the day, underneath all the algorithms are people. And we influence the algorithms just as much as they may influence us.

Lewis Mitchell, Senior Lecturer in Applied Mathematics and James Bagrow, Associate Professor, Mathematics & Statistics, University of Vermont